Distributed kernel regression: An algorithm for training collaboratively

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22 Scopus citations

Abstract

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.

Original languageEnglish (US)
Title of host publication2006 IEEE Information Theory Workshop, ITW 2006
Pages332-336
Number of pages5
StatePublished - Nov 21 2006
Event2006 IEEE Information Theory Workshop, ITW 2006 - Punta del Este, Uruguay
Duration: Mar 13 2006Mar 17 2006

Publication series

Name2006 IEEE Information Theory Workshop, ITW 2006

Other

Other2006 IEEE Information Theory Workshop, ITW 2006
CountryUruguay
CityPunta del Este
Period3/13/063/17/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Predd, J. B., Kulkarni, S. R., & Poor, H. V. (2006). Distributed kernel regression: An algorithm for training collaboratively. In 2006 IEEE Information Theory Workshop, ITW 2006 (pp. 332-336). [1633840] (2006 IEEE Information Theory Workshop, ITW 2006).